Segregating variation in the transcriptome: cis regulation and additivity of effects.
ABSTRACT Properties of genes underlying variation in complex traits are largely unknown, especially for variation that segregates within populations. Here, we evaluate allelic effects, cis and trans regulation, and dominance patterns of transcripts that are genetically variable in a natural population of Drosophila melanogaster. Our results indicate that genetic variation due to the third chromosome causes mainly additive and nearly additive effects on gene expression, that cis and trans effects on gene expression are numerically about equal, and that cis effects account for more genetic variation than do trans effects. We also evaluated patterns of variation in different functional categories and determined that genes involved in metabolic processes are overrepresented among variable transcripts, but those involved in development, transcription regulation, and signal transduction are underrepresented. However, transcripts for proteins known to be involved in protein-protein interactions are proportionally represented among variable transcripts.
- SourceAvailable from: Rita Marie Graze[Show abstract] [Hide abstract]
ABSTRACT: The mechanistic basis of regulatory variation and the prevailing evolutionary forces shaping that variation are known to differ between sexes and between chromosomes. Regulatory variation of gene expression can be due to functional changes within a gene itself (cis), or in other genes elsewhere in the genome (trans). The evolutionary properties of cis mutations are expected to differ from mutations affecting gene expression in trans. We analyze allele-specific expression across a set of X substitution lines in intact adult Drosophila simulans to evaluate whether regulatory variation differs for cis and trans, for males and females, and for X-linked and autosomal genes. Regulatory variation is common (56% of genes), and patterns of variation within D. simulans are consistent with previous observations in Drosophila that there is more cis than trans variation within species (47% vs. 25%, respectively). The relationship between sex-bias and sex-limited variation is remarkably consistent across sexes. However, there are differences between cis and trans effects: cis variants show evidence of purifying selection in the sex towards which expression is biased, while trans variants do not. For female-biased genes, the X is depleted for trans variation in a manner consistent with a female-dominated selection regime on the X. Surprisingly, there is no evidence for depletion of trans variation for male-biased genes on the X. This is evidence for regulatory feminization of the X, trans acting factors controlling male-biased genes are more likely to be found on the autosomes than those controlling female-biased genes.Genome Biology and Evolution 04/2014; · 4.76 Impact Factor
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ABSTRACT: Mapping expression quantitative trait loci (eQTL) of targeted genes represents a powerful and widely adopted approach to identify putative regulatory variants. Linking regulation differences to specific genes might assist in the identification of networks and interactions. The objective of this study is to identify eQTL underlying expression of four gene families encoding isoflavone synthetic enzymes involved in the phenylpropanoid pathway, which are phenylalanine ammonia-lyase (PAL; EC 184.108.40.206), chalcone synthase (CHS; EC 220.127.116.11), 2-hydroxyisoflavanone synthase (IFS; EC:18.104.22.168) and flavanone 3-hydroxylase (F3H; EC 22.214.171.124). A population of 130 recombinant inbred lines (F5:11), derived from a cross between soybean cultivar 'Zhongdou 27' (high isoflavone) and 'Jiunong 20' (low isoflavone), and a total of 194 simple sequence repeat (SSR) markers were used in this study. Overlapped loci of eQTLs and phenotypic QTLs (pQTLs) were analyzed to identify the potential candidate genes underlying the accumulation of isoflavone in soybean seed.BMC genomics. 08/2014; 15(1):680.
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ABSTRACT: One method of identifying cis regulatory differences is to analyze allele-specific expression (ASE) and identify cases of allelic imbalance (AI). RNA-seq is the most common way to measure ASE and a binomial test is often applied to determine statistical significance of AI. This implicitly assumes that there is no bias in estimation of AI. However, bias has been found to result from multiple factors including: genome ambiguity, reference quality, the mapping algorithm, and biases in the sequencing process. Two alternative approaches have been developed to handle bias: adjusting for bias using a statistical model and filtering regions of the genome suspected of harboring bias. Existing statistical models which account for bias rely on information from DNA controls, which can be cost prohibitive for large intraspecific studies. In contrast, data filtering is inexpensive and straightforward, but necessarily involves sacrificing a portion of the data.BMC genomics. 10/2014; 15(1):920.
Copyright ? 2006 by the Genetics Society of America
Segregating Variation in the Transcriptome: Cis Regulation and
Additivity of Effects
Kimberly A. Hughes,*,†,1Julien F. Ayroles,‡,2Melissa M. Reedy,* Jenny M. Drnevich,*,3
Kevin C. Rowe,*,4Elizabeth A. Ruedi,§Carla E. Ca ´ceres* and Ken N. Paige*
*School of Integrative Biology,†Institute for Genome Biology,‡Department of Natural Resources and Environmental Sciences
and§Program in Ecology and Evolutionary Biology, University of Illinois, Urbana, Illinois 61801
Manuscript received September 23, 2005
Accepted for publication April 12, 2006
Properties of genes underlying variation in complex traits are largely unknown, especially for variation
that segregates within populations. Here, we evaluate allelic effects, cis and trans regulation, and domi-
nance patterns of transcripts that are genetically variable in a natural population of Drosophila melanogaster.
Our results indicate that genetic variation due to the third chromosome causes mainly additive and nearly
additive effects on gene expression, that cis and trans effects on gene expression are numerically about
equal, and that cis effects account for more genetic variation than do trans effects. We also evaluated
patterns of variation in different functional categories and determined that genes involved in metabolic
processes are overrepresented among variable transcripts, but those involved in development, tran-
scription regulation, and signal transduction are underrepresented. However, transcripts for proteins
known to be involved in protein–protein interactions are proportionally represented among variable
viduals and is the raw material for evolution, yet very
little is known about the characteristics of genes under-
lying this variation. Further, it is not clear what evolu-
tionary processes maintain within-population variation
in the face of natural selection and genetic drift (Barton
and Turelli 1989; Lynch et al. 1998; Charlesworth
and Hughes 2000; Turelli and Barton 2004), and
models to explain the maintenance of variation depend
on parameters that have been difficult to estimate
(Charlesworth and Hughes 2000).
Addressing these questions depends on measuring
the effects and interactions of genes that segregate
within natural populations (Mackay 2001). However,
reliable and unbiased data have been difficult to obtain
because it is not usually possible to measure phenotypic
effects of segregating alleles, except in rare cases of
Mendelian segregation of discrete phenotypes. Quanti-
tative trait loci (QTL) mapping studies can estimate
allelic effects; however, most QTL have not been re-
ITHIN-population genetic variability is a major
source of phenotypic differences among indi-
investigated between- rather than within-population
variation (Glazier et al. 2002).
Oligonucleotide microarrays provide a novel tool for
investigating within-population variation because they
allow the simultaneous sampling of large numbers of
phenotypes (mRNA abundance of thousands of differ-
ent transcripts). A high proportion of these phenotypes
only moderate numbers of individuals are sampled
(Oleksiak et al. 2002; Townsend et al. 2003; Wayne
genetic and phenotypic variation, cis and trans regula-
tion, and dominance for .18,000 genes in adult male
Drosophila melanogaster. We found that cis-regulatory ef-
fects predominate for transcripts with high levels of
genetic variation and that variable transcripts tend to be
involved in metabolic processes, but not in develop-
mental or regulatoryprocesses. Wealso found that most
transcripts exhibited within-locus additivity or near ad-
ditivity with respect to mRNA abundance, and few dis-
played dominance or overdominance.
MATERIALS AND METHODS
Experimental flies: We obtained third-chromosome sub-
stitution lines of D. melanogaster from J. Leips (University of
Maryland, Baltimore County) for this experiment. Within
1Corresponding author: School of Integrative Biology, University of
Illinois, 515 Morrill Hall, 505 S. Goodwin Ave., Urbana, IL 61801.
2Present address: Department of Genetics, North Carolina State Univer-
sity, Raleigh, NC 27695.
3Present address: Roy J. Carver Biotechnology Center, Keck Center for
Functional Genomics, University of Illinois, Urbana, IL 61801.
4Present address: Department of Biological Science, Florida State Uni-
versity, Tallahassee, FL 32206.
Genetics 173: 1347–1355 (July 2006)
a different wild-type C3 derived from a single natural pop-
ulation in Raleigh, North Carolina (De Luca et al. 2003). All
other chromosomes were identical across lines and were
derived from the highly inbred SAM stock (Lyman et al.
1996). Thus, these isogenic lines differed only with respect to
allelic variation of genes on C3. We checked homozygosity of
the isogenic lines by typing each line for a set of seven variable
microsatellite loci (four on C3 and three on C2). All were
homozygous within each line.
We measured transcript abundance in equal-aged adult
males from six different isogenic lines. We also intercrossed
three of these lines (lines 33, 83, and 483) in all possible
combinations to produce F1flies that were hybrids for alleles
33 males) produced flies that were identical for the nuclear
genome. To eliminate differences between parental and F1
lines due to maternal effects, we pooled offspring from recip-
rocal crosses and mixed them in equal numbers before ex-
tracting RNA. These pooled F1flies were designated as lines
33 3 83, 33 3 483, and 83 3 483.
For each isogenic and F1line, we reared offspring from
replicate vials produced from two independent sets of parents
(block A and block B) to produce true biological replicates.
We housed block A flies in one incubator and block B flies in
another; both blocks were reared and collected at the same
time. Parents of experimental flies were reared from constant-
density vials (7 males and femalesper vial), and males used for
expression analysis were reared at a constant larval density of
25 per vial. Over a 2-day period (days 0 and 1), we collected
males within 8 hr of eclosion and kept them in single-sex vials
at low density (10 males per vial) until day 4 when all males
were 3–4 days posteclosion. Because we wanted to assay males
females from a laboratory strain (e/e on Ives outbred back-
ground) beginning at day 4 and continuing until they were
preserved for mRNA extraction on day 8. These mating vials
were established at a density of 3 males and 3 females per vial,
and four independent vials were maintained per genotype per
Onday 8, wechose males from each genotype and block for
extraction of mRNA, for a total of 18 extractions. For each
extraction, we pooled six males chosen from the four mating
vials in the appropriate block. Two males were chosen from
each of two mating vials and one male from each of the two
other vials. Males were flash frozen in random order between
13:00 and 15:00 hr CST. We extracted RNA from all block
A flies on the same day and from all block B flies on the next
day. Standard Trizol protocols were used for extraction and
labeling with the MessageAmp aRNA kit. We hybridized
labeled mRNA to Affymetrix Drosophila 2.0 GeneChips ac-
cording to manufacturer’s protocols and scanned them with
an Affymetrix GeneArray Scanner at the University of Illinois
Affymetrix Core Facility.
Transcript abundance and genetic variation: Affymetrix Dro-
sophila 2.0 GeneChip contains probes for 18,769 transcripts
with 14 probes per transcript. Probe-level intensity values were
obtained for each array from MAS 5.0, and all probe intensity
values were standardized to the mean value for the array. We
determined that 14,298 transcripts were detectable in experi-
mental flies by comparing signals from perfect-match (PM)
probes to those from mismatch probes across all arrays using a
Wilcoxon sign-rank test (n ranged from 162 to 213). This is
probably a liberal test for transcript presence, but we deemed
this appropriate because it includes more genes in the entire
analysisthana moreconservative test,sothe false discovery rate
(FDR, the expected proportion of significant results that are
the remainder of the tests we applied are conservative. We used
a cutoff of P , 0.04 for determining that a transcript was
present, for an FDR of 0.05.
PM intensity values from the 14,298 detectable transcripts
(Chu et al. 2002). The consistency of probe-level measures
from independent replicates of the same genotype was high
(Pearson’s r ¼ 0.94), indicating high repeatability of expres-
sion measurements. We tested for significant variation in
mRNA abundance among the C3 substitution lines, using
gene-specific mixed linear models of the form Log2(PM) ¼
m 1 L 1 P 1 L 3 P 1 B(L) 1 e, where L was the line effect,
P the probe effect, and B(L) the effect of block nested within
line. L and P were fixed effects and B(L) was random to pro-
vide the error term for L. These models were implemented in
SAS PROC MIXED V. 9.1 (SAS Institute 2002). Probe-level
values were deleted as outliers if they had external Student-
ized residuals .3. After outlier removal, 13,423 of the tested
transcripts (94%)hadresidualsthat werenormally distributed
(Shapiro–Wilk P . 0.05). For genes with significant among-
line variation, we calculated genetic variance in transcript
abundance (VG) as the among-line variance component from
fects of line and block within line. To compare genetic varia-
tion among transcripts with different abundance levels, we
where x was the mean abundance of the transcript.
Cis and trans regulatory effects: Because the only allelic
differences between C3 substitution lines were attributable to
genes on C3, all genetic variation in transcript abundance was
due to sequence variation on C3. Variable expression of genes
on other chromosomes must have been due to trans-acting
effects of alleles on C3. This does not imply that the variable
genes on other chromosomes have no cis-regulatory mecha-
nisms, only that cis effects would not have contributed to
genetic variation in our experiment. Variation in abundance
of C3 transcripts could be caused either by cis- or by trans-
acting sequence differences, but the proportion subject to
trans regulation can be estimated from the number of variable
transcripts onthe other chromosomes. We therefore obtained
chromosomal locations of genes producing each transcript
with significant genetic variation, to determine the contribu-
tions of cis- and trans-regulatory effects. Chromosomal loca-
tions were obtainedfrom the Affymetrix database onFebruary
3, 2005 (Liu et al. 2003).
a transcript occurring on C3 was significantly related to the
magnitude of genetic variation as measured by CVG. However,
the logistic regression assumes that all transcripts are in-
dependent, so the test might be too liberal. We therefore
applied a conservative contingency-table test (Wayne et al.
2004), by dividing the 2329 variable transcripts into thirds
(tertiles) on the basis of their CVGvalues and using x2-tests to
determine if chromosomes were equally represented among
tertiles (Table 1).
To determine the total number of cis- vs. trans-regulated
transcripts, we also calculated the expected proportion of C3
genes that were trans regulated, assuming that the locations of
trans-regulatory variants are randomly distributed relative to
their target genes, which is consistent with data from yeast
(Brem et al. 2002; Yvertet al. 2003). We based the estimate of
trans-regulated C3 genes on the proportion of trans-regulated
genes on the other major chromosomes in D. melanogaster (C2
Functional classification and interacting transcripts: To test
genetically variable transcripts, we used the Affymetrix gene
1348K. A. Hughes et al.
ontology (GO) mining tool (Liu et al. 2003). The database
contains biological process annotation for 6895 genes, of
it contains molecular function annotation for 7240 genes, of
which 1086 were variable. We tested the following GO func-
tional categories for over- or underrepresentation among
genetically variable genes: ‘‘development,’’ ‘‘regulation,’’ ‘‘me-
tabolism,’’ and ‘‘cell communication’’ for biological process
and ‘‘catalytic (enzymatic) activity,’’ ‘‘signal transduction,’’
‘‘regulation of transcription,’’ and ‘‘structural molecule’’ for
molecular function (Rifkin et al. 2003). We tested for dispro-
portional representation of functional categories by compar-
ing the frequency of each category among genetically variable
transcripts to its expected frequency among all detectable
transcripts. We used x2-tests for this analysis, because of
the potential for nonindependence among transcripts in the
same functional category. A category was judged to under- or
overrepresented if P , 0.01. Less than 1 false positive is ex-
pected under this criterion.
We also compared variable and nonvariable transcripts with
respect to evidence for interactions among the protein
products of the genes. Giot et al. (2003) reported .20,000
interactions between 7048 proteins encoded in the D. mela-
or low confidence to these interactions, with high confidence
assigned to 4780 interactions involving 4679 proteins. We
compared genetically variable and nonvariable transcripts to
the entire list of interacting proteins and, separately, to the
high-confidence list and tested for nonrandom associations
using a two-by-two contingency table analysis.
Dominance: To calculate dominance effects, we compared
mRNA abundance in isogenic lines with that of the F1hybrid
offspring they produced. We first determined which tran-
scripts were variable among the isogenic and F1lines that we
used in crosses: 33, 83, 483, 33 3 83, 33 3 483, and 83 3 483,
using the linear model described above. We found that 905
transcripts were significantly variable among these lines at
P , 0.013 (FDR of 0.2). For these 905 transcripts, we then
determined if there was significant variation among lines that
composed a cross (e.g., pairwise differences between lines 33,
83, and 33 3 83), using ESTIMATE statements in PROC
MIXED and a P-value cutoff of 0.05. If there were pairwise
differences, we calculated the dominance of transcript abun-
dance as d/a, where a is half the difference in abundance
between the isogenic parental lines and d is the difference
between the F1hybrid and the mean of the parental lines
(Falconer and Mackay 1996; Gibson et al. 2004). If tran-
script abundance in the hybrid is exactly intermediate to that
in the parental lines, d ¼ 0; thus d/a ¼ 0 indicates within-locus
additivity and |d/a| ¼ 1 indicates complete dominance. If
is indicated, meaning the hybrid falls outside the range of
phenotypes spanned by the parents.
Wedetermineif mRNAabundanceinthetwoparental lines
was significantly different using ESTIMATE statements in
PROC MIXED, equivalent to testing 2a . 0. We also de-
termined if the hybrid was significantly different from the
mean of its parental lines, equivalent to testing |d| . 0 and
indicating significant deviation from within-locus additivity.
Type III F-tests for both effects had 2 numerator d.f. and 3
denominator d.f. This is an appropriate test for dominance if
the dependent variable is linearly related to transcript abun-
dance. Benchmark trials have shown that log2(PM) is linearly
related to log2(abundance) (Cope et al. 2004) with slope
estimates between 0.67 and 0.87. For the range of values we
observed (4.8 , log2(PM) , 11.6), log2(PM) and abundance
are nearly linearly related. We also calculated dominance by
assuming a linear relationship between PM and abundance;
the results were nearly identical. We therefore report only
results obtained on the log scale because of better distribu-
tional properties of all variables on this scale.
To determine if transcripts displayed significant overdom-
inance, we calculated 80 and 95% confidence intervals for a
and d using ESTIMATE statements within PROC MIXED. If
upper and lower limits for d and a did not overlap, we con-
cluded that there was statistical support for overdominance.
Cis and trans regulatory effects: Of the 14,298 detect-
able transcripts, 2329 (16.3%) showed significant vari-
ation among isogenic lines at P , 0.01 (FDR ¼ 0.06,
supplemental Table 1 at http:/ /www.genetics.org/
from 19.0 to 0.3. Genes located on C3 were substantially
overrepresented among genetically variable transcripts:
C3 accounted for 44% (6338) of all detectable tran-
scripts and for 72% (1683) of the genetically variable
sistent with substantial cis regulation of genetically var-
C3 was also overrepresented among the transcripts
exhibiting the most variation when the analysis is
limited to the 2329 transcripts with significant genetic
variation (Figure 1). The probability of a transcript
occurring on C3 was positively related to CVG(likeli-
hood ratio x1
ofatranscript occurringontheotherchromosomes was
0.0001; chromosome 2 x1
Using the tertile method, the representation of C3 was
significantly heterogeneous (x2
with 82% of transcripts in the highest tertile occurring
on that chromosome, compared to 74% in the middle
tertile and 60% in the bottom tertile (Table 1).
Of 5410 C2 transcripts that were detectable in our
study, 81 (1.5%) were genetically variable and in the top
tertile of all CVGvalues, 153 (2.8%) were variable and in
2¼ 626, P , 10?5). This pattern is con-
2¼ 61.4, P , 0.0001), while the probability
2¼ 43.8, P , 0.0001) (Table 1).
2¼ 94.2, P , 0.0001),
Figure 1.—Coefficient of genetic variation (CVG) in tran-
script abundance, vs. the probability that the transcript is en-
coded by a gene on C3. The probabilities were calculated
from a logistic regression analysis.
Segregating Variation in Transcriptome1349
the middle tertile, and 203 (3.8%) were variable and in
the bottom tertile (supplemental Table 1 at http:/ /www.
genetics.org/supplemental/). The proportions were
very similar for the 2250 detectable X chromosome
the middle, and 92 (4.1%) were in the bottom tertile of
all genetically variable transcripts. Taking the propor-
tion of trans-regulated genes on X and C2 as the pro-
portion of detectable transcripts that are expected to
be trans regulated by genes on C3 and applying these
proportions to the detectable C3 transcripts, we expect
0.0156 3 6338 ¼ 99 C3 transcripts in the top tertile,
0.0125 3 6338 ¼160 in the middle tertile, and 0.0385 3
6338 ¼ 244 in the bottom tertile of genetically variable
transcripts to be trans regulated by other genes on C3
2329 ¼ 51% of the genetically variable transcripts.
However, it accounts for 70% of all transcripts in the
top tertile of genetic variation, 53% of transcripts in the
middle tertile, and 29% of those in the bottom tertile
(Table 1). Because the mean standardized genetic var-
iance (CVG)2for transcripts in the top tertile is 5.4 times
higher than that for transcriptsin the middle tertile and
26 times higher than that for transcripts in the bottom
tertile (Table 1), we conclude that cis effects are re-
sponsible for substantially more genetic variance in
gene expression than are trans effects and consequently
(Meiklejohn et al. 2003; Wayne et al. 2004).
To determine if variation in mRNA sequences among
(Hsieh et al. 2003), we filtered the 2329 genetically
variable transcripts on the basis of a measure of the
average between-line correlation of hybridization sig-
nals within an individual probe: Cronbach’s a (SAS
Institute 2002). For each gene on each array, we
sequentially deleted probes with the largest values of
external Studentized residuals (rS) from the original
linear model until the value of Cronbach’s a exceeded
0.90. We filtered all genes that did not achieve this level
of a after deleting probes with rS# 1.0. This left 1712
genes with known chromosomal locations, of which 112
nearly the same proportion of genes occurs on C3 as on
the unfiltered list of variable genes. Sequence variation
at probe sites apparentlydid notbias our estimateofthe
proportion of cis- and trans-regulated transcripts.
Functional classification and interacting transcripts:
Classification of genetically variable transcripts into
biological process categories indicated that develop-
mental, cell communication, and regulatory processes
were significantly underrepresented, compared to their
Metabolic processes were overrepresented, but not
significantlyso.Two molecular functioncategories were
also underrepresented: signal transduction and tran-
scription regulation. Structural molecules were also
underrepresented, but the deviation was nonsignificant
by the conservative contingency-table analysis we used.
Catalytic activity was the only molecular function cate-
gory that was overrepresented.
When we compared variable and nonvariable tran-
scripts with respect to the number of interacting pro-
teins, we found no significant associations. Of the 2329
variable transcripts, 1115 are associated with proteins
known to be involved in interactions, and 1214 are not.
Of the 11,969 transcripts that were detectable in our
sample but not genetically variable, 5533 were associ-
is thus no evidence of association between variability
and interaction (x1
stricted theanalysis to high-confidence protein–protein
interactions (Giot et al. 2003), the results were similar:
731 variable transcripts were associated with high-
confidence interacting proteins, and 1598 were not,
while the numbers for nonvariable transcripts are 3690
of 8279, respectively. Again, there is no evidence of
mean number of interactions per transcript is nearly
identical for variable and nonvariable transcripts. Vari-
able transcripts average 0.64 (SE ¼ 0.03) interactions
per transcript and nonvariable ones average 0.60 (SE ¼
Dominance of transcript abundance: We detected at
least one pairwise difference between the parental and
2¼ 2.13, P ¼ 0.14). When we re-
2¼ 0.28, P ¼ 0.59). Furthermore, the
Chromosomal location of transcripts in each tertile of genetic coefficient of variation
X C2Other TotalMean CVG
aTertile 1 comprises transcripts with the most genetic variation in expression; tertile 3 comprises those with
bThe mean standardized genetic variance for each tertile is the square of the mean CVG.
cExpected number of false positives is less than two in all categories.
1350 K. A. Hughes et al.
F1lines in 1589 cases (646 in cross 33 3 83, 620 in cross
significantly different from each other (i.e., a . 0) in
1549 of these cases. The hybrid was significantly differ-
ent from the mean of the parental lines (i.e., |d| . 0) in
only 205 cases. This disparity was not due to differential
power of the tests, because they had the same degrees of
freedom, median standard errors were not strikingly
different (SE[d] ¼ 0.04; SE[a] ¼ 0.03), but medians of
the estimates themselves were quite different (median
d ¼ 0.06, median a ¼ 0.19). Distributions for a and d are
shown in Figure 2, A and B, and all values of standard
errors are provided in supplemental Table 2 at http:/ /
Distributions of dominance values are shown in
Figure 2C. Median values were ?0.03 (Wilcoxon test,
P .0.50), 0.08 (P,0.001),and 0.14 (P,0.001)for the
fell between ?0.5 and 10.5, indicating additivity or
intermediate dominance. Overall, 1128 transcripts had
nance (0 , |d/a| , 0.5), 330 had values consistent with
intermediate-to-complete dominance (0.5 , |d/a| , 1),
and 131 had values consistent with overdominance
(|d/a| . 1).
Of the apparently overdominant transcripts, seven
were significantly overdominant using a stringent crite-
rion of nonoverlap of the 95% confidence intervals of
d and a; 30 were significant using 80% confidence
intervals. A previous study of dominance of mRNA
abundance phenotypes used a cutoff of |d/a| . 1.32 on
the log2(PM) scale, instead of confidence limits, to
indicate overdominance (Gibson et al. 2004). This ap-
proach protects against underestimation of the number
of overdominant transcripts because of low statistical
power. Using this criterion, 88 (3.7%) transcripts dis-
played overdominance, in broad agreement with the
results of Gibson et al. (2004). There were few tran-
scripts with |d/a| . 3, and all were associated with small
values of a (Figure 3). Excluding values of a , 0.1, there
were no significant associations between a and |d/a|
(cross 33 3 83, Pearson’s r ¼ ?0.04, P ¼ 0.39, N ¼ 546;
83 and 83 3 483, r ¼ ?0.03, P ¼ 0.62, N ¼ 265).
Over 16% of the transcripts present in adult males
demonstratedsignificant geneticvariancein expression
within a single population when only C3 (?40% of the
genome) varied among genotypes. This is only slightly
smaller than the proportion of genes showing within-
population phenotypic variation in Fundulus (18%,
Oleksiak et al. 2002) and/or the proportion of genes
varying between inbred lines from different source
populations in D. melanogaster (25%, Gibson et al.
2004). This comparison suggests that quantitative ge-
netic variation for mRNA abundance is at least as
prevalent within populations as it is between popula-
tions of D. melanogaster. Our results indicate that within-
the segregation of many nearly additive alleles, that,
numerically, cis effects account for about half the
genetically variable transcripts, and that cis effects
contribute substantially more to genetic variance in
Gene ontology (GO) categories under- or overrepresented among genetically variable transcripts
Gene ontology categoryGenetically variablePresent in adult males
1024 All genes annotated for biological process
Regulation of biological process
1086 All genes annotated for molecular function
Significant categories (two-sided P , 0.0001) are indicated in italics.
aCalculated by contingency-table analysis, comparing the representation of a category among genetically variable transcripts to
its representation among all annotated transcripts present in our sample.
Segregating Variation in Transcriptome1351
expression that do trans effects. Previous studies of
modes of regulation have yielded mixed results, with
some reporting a high proportion of trans regulation
(Montooth et al. 2003; Yvert et al. 2003; Wayne et al.
2004; Harbison et al. 2005) and some reporting mostly
evaluated regulation by using between-population or
of between- than of within-population variation, since
selection acting within populations may strictly limit
variation in trans-acting genes with multiple down-
stream effects (Denver et al. 2005).
One previous study of within-population variation in
D. simulans also reported evidence for both cis and trans
regulation, and found that trans effects were more
numerous than cis effects (Wayne et al. 2004), but that
cis effects were larger than trans effects (a result also
reported by Meiklejohn et al. 2003). Thus there are
both intriguing differences and similarities between the
two studies. We detected a higher proportion of avail-
able transcripts on the Affymetrix chip (76% vs. 56%), a
difference that could be explained by our use of a more
liberal statistical threshold for transcript presence or by
a loss of detectability in the D. simulans study due to
to a difference between first and second-generation
Affymetrix arrays (we used the newly available second-
genetically variable transcripts overall (16% vs. 8%).
Our lines varied only for C3, which accounts for ?40%
of the D. melanogaster genome, meaning that we would
have detected an even higher proportion of variable
Figure 2.—Values of a (A), d (B), and dominance, d/a (C), for transcripts that had at least one pairwise difference in expression
between members of a cross. The results for three crosses are shown. Solid areas indicate values for which the parameter was
significantly different from 0 (P , 0.05), and open areas indicate nonsignificant estimates. Note the difference in vertical scale
for cross 83 3 483.
1352K. A. Hughes et al.
transcripts had our lines differed for all chromosomes.
However, a likely explanation of this difference is that
different genetic components of variation were mea-
sured in the two studies (additive variance in the pre-
Homozygous genetic variance is expected to exceed
additive variation within populations (Charlesworth
and Hughes 1996), and experiments confirm this ex-
pectation (Hughes 1995a,b; Hughes et al. 2002). Thus
our observation of more genetically variable transcripts
might simply reflect higher power to detect the larger
variances associated with inbred lines.
We also detected an approximately equal numerical
representation of cis- than of trans-acting variation in
are on average larger than trans. We could detect only
transcript variation that was caused by sequence varia-
tion on C3. If patterns of cis and trans regulation differ
between chromosomes, and particularly if they differ
between sex chromosomes and autosomes, our results
would not be representative of sex-linked variation. We
of regulation, however. Further, genetic variation on
other chromosomes would be likely to induce some
additional trans effects on C3, although interchromo-
somal epistatic interactions could also increase the level
of cis-acting variation. Studies comparing chromosome
extraction lines directly to whole-genome inbred lines
from the same population could be used to address this
issue. Finally, the two sets of results are reconcilable
if genes that are highly variable within D. melanogaster
(mostly cis regulated) are also characterized by greater
sequence divergence between species and are therefore
less likely to be detected in cross-species hybridizations.
Evidence for a positive correlation of within-species
variation and between-species divergence has come
from studies of genes involved in reproduction (Begun
et al. 2000) and cell-surface proteins (Lazzaro 2005);
evaluation of the genomewide pattern awaits genome
sequencing of other Drosophila species.
Functional analysis of genetically variable transcripts
provides insight into the selective constraints that operate
on within-population variation. Transcripts involved in
Figure 3.—Values of d/a vs. a. Five points are not shown to
retain reasonable detail in the graph. These points are, for
cross 33 3 83, a, d/a pairs, 0.002, 154; 0.002, 86.8; 0.014,
42.4; 0.004, 40.3; and for cross 33 3 483 a, d/a pairs, 0.012,
22.7. Red symbols indicate transcripts for which d was signif-
icantly different from 0; black symbols indicate transcripts for
which d was not significant. Horizontal bars show standard
errors for a; vertical bars show standard error for d/a (calcu-
lated by propagating of error). Note the different scales for a
Segregating Variation in Transcriptome1353
metabolic/catalytic processes tend to be genetically
variable, while those involved in development, tran-
scriptional regulation, and signal transduction do not.
Metabolic transcripts may be more variable than devel-
opmental and regulatory ones because they are sub-
ject to weaker purifying selection or because variation
is actively maintained in metabolic enzymes by hetero-
zygote advantage, genotype–environment interaction,
or other forms of balancing selection. Strong purify-
ing selection on developmental and regulatory tran-
scripts may be common if small deviations in transcript
abundance disrupt highly integrated developmental
and regulatory pathways. Conversely, balancing se-
lection mediated by a variable environment could be
particularly relevant to metabolic enzymes, which me-
diate interactions directly between the organism and its
Genetic variation is not disproportionately repre-
sented among transcripts that produce proteins that
interact with other proteins. Because transcriptional,
translational, and metabolic proteins are all enriched
among those engaged in protein–protein interactions
(Giot et al. 2003), and metabolic genes are likely to be
genetically variable, but regulatory genes are not, this
result is not too surprising. However, our results do
suggest thatproteininteractionitself doesnotconstrain
or enhance within-population genetic variation.
Dominance patterns indicate that overdominance is
rare, and within-locus additivity is typical of segregating
variation in transcript abundance. Over 97% of variable
transcripts had significant expression differences be-
tween two homozygous parents, but only 13% had F1
values that deviated significantly from the midpoint of
parental values. There is no obvious relationship be-
tween |d/a| and a, as might be expected if genes with
large differences between parental lines tend to be
dominant or overdominant. This preponderance of
additivity contrasts with results from a recent study of
crosses between inbred lines derived from different
source populations (Gibson et al. 2004). In our study,
71% of dominance values fell between ?0.5 and 10.5,
while only 15% of values fell in that range in the
previous study (calculated from supplemental informa-
tion for males in Gibson et al. 2004). It might be argued
that this difference derives from pooling of reciprocal
crosses in our experiment, since Gibson et al. detected
differences between F1reciprocal males in 9% of tran-
scripts tested. However, that study crossed inbred lines
that differed at all chromosomes. Male flies from
reciprocal crosses thus had different X (and Y) chro-
mosomes, and this could account for differential
expression in reciprocal males and for some cases of
apparent dominance in males in that experiment. In
our study, all lines had the same X, C2, and Y chro-
mosomes, so males from reciprocal crosses had identi-
cal genotypes and should have identical expression
patterns, aside from maternal effects.
Another possible contributor to different dominance
patterns in the two studies is that different filtering
criteria were used. Gibson et al. (2004) included a
transcript in all dominance calculations if at least one
12 genotype/sex categories, even though any given
dominance value was calculated from only 3 of the 12
categories. We used a more stringent criterion and
calculated dominance values only for transcripts show-
ing significant differences among the three genotypes
actually used in the dominance calculation.
Because these experimental differences are unlikely
to completely account for the large differences in dom-
inance patterns in the two studies, we believe that the
discrepancy reflects real differences in the genetic
architecture of the lines used. The most obvious dif-
ference is that we investigated a random sample of
within-population variation, while Gibson et al. used
crosses between inbred lines derived from two different
source populations (Ore-R and 2b). Although there
appears to be limited molecular population structure in
D. melanogaster outside of Africa (Dieringer et al. 2005),
substantial population differentiation has been found
among non-African populations for many quantitative
traits(DeJongand Bochdanovits2003; Schmidt etal.
2005), even on a microgeographic scale (Wayne et al.
2005). In addition, neutral markers have been shown
to drastically underestimate population structure for
adaptive traits in other species (Karhu et al. 1996). We
therefore suggest that differences in the genetic archi-
tecture of nonneutral quantitative traits might depend
critically on the level at which the variation is investi-
gated (i.e., between vs. within populations).
Alternately, the process of inbreeding itself might
lead to different genetic architectures. The lines used
by Gibson et al. were produced by many generations of
brother–sister mating, which provides an extended op-
portunity for selection to operate. Furthermore, one of
the lines, 2b, underwent selection for low male mating
ability during the course of inbreeding. Our lines were
a natural population using balancer chromosomes.
Third chromosomes that were homozygous lethal or
that had very low homozygous fitness would have been
removed by this process, but selection was otherwise
minimized, and most genetic variation would havebeen
preserved in these lines.
Characterizing effects and interactions of alleles that
segregate in natural populations is critical for under-
standing the genetic basis of phenotypic variation. Our
results indicate that the genetic architecture of within-
population variation might be different from that
observed between populations and that there is a real
need for more information on within-population vari-
ation. Ultimately, accurate descriptions of variation at
both levels will be required for a complete understand-
ing of the causes of genetic and phenotypic diversity.
1354K. A. Hughes et al.
We thank and C. Hartway, J. Leips, C. Milling, C. Whitfield, M.
Whitlock, and two anonymous reviewers for insightful comments on
manuscript drafts and C. Wilson for conducting the Affymetrix
hybrizations and scanning. This work was supported by National
Science Foundation awards DEB-0296177 (K.A.H.) and DEB-0092554
(K.N.P.) and by the University of Illinois Urbana Campus Research
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Communicating editor: D. M. Rand
Segregating Variation in Transcriptome 1355